- Budget Optimizer
- Improved campaigns ROI
- Sales decomposition
Scope
Geography: 4-6 Brands for each geography — South Africa, Australia, USA, Thailand and China
Model
Country X Brand X Region Level X Channel & sub-channel X Campaign Level Insights
Applications
Overview
Our client, a Fortune 50 multinational food, snacks, and beverages corporation with an annual revenue of over 70 billion USD (as of 2020) and with presence in nearly 150 countries and territories, spends billions of dollars annually in its global marketing and advertising.
Client wanted to improve the efficiency of its marketing factors by identifying the contribution and ROI of various marketing factors such as advertisements (GRPs), newspaper ads, YouTube ads, and magazine ads. Client wanted to understand the impact of various marketing channels and their sub-components on total sales and to determine an optimal marketing mix.
Solution
TransOrg developed market mix models by using advanced analytics and machine learning techniques covering four-six brands across South Africa, Australia, USA, Thailand, and China regions to provide granular level insights at the ‘Country X Brand X Region’ level and at the ‘Channel X Sub-Channel X Campaign’ level.
Models are refreshed either quarterly, half yearly or yearly depending upon the sales salience.
For each region mentioned above the input data used for building the models included:
- Internal shipment data
- Nielsen sales data with appropriate volume corrections based on internal
shipment data - Media data such as GRPs, impressions etc.
- Trade promotion and competition price
- Macro-economic factors such as GDP, growth rate, inflation etc.
Advanced statistical methods were used to quantify the incremental impact of each marketing category on sales and to attain optimum spend in each marketing category. Further, a drill down was approach was followed to monitor effectiveness of each channel, sub-channel and campaign.
Output
TransOrg helped the markeing team of the client to boost their marketing returns by 5% by realigning the budget allocation for different channels.
